Paper short abstract

This paper examines how gamified incentives in ride-hailing platforms reshape drivers' work ethics in Cluj-Napoca. Drivers perceive algorithmic prompts as motivation, yet this produces overwork and dependency, revealing tensions in defining "good work" within digitally mediated platform labor.

Paper long abstract

As ride-hailing platforms restructure labor through gamified interfaces, notions of "good work" are being redefined in contested ways. This paper examines how Uber and Bolt drivers in Cluj-Napoca, Romania, negotiate autonomy, productivity, and care within algorithmically managed work environments. Drawing on 11 semi-structured interviews, 115 ride-along observations, and participation in a driver protest, the study traces how platforms gamified incentives synchronize driver labor with urban rhythms and platform profit imperatives.

Drivers perceive these prompts as motivational opportunities rather than coercion, illustrating how algorithmic management cultivates a "machine habitus" that naturalizes self-optimization. Yet beneath this perceived flexibility lies manufactured dependence: volatile earnings and intermittent bonuses compel drivers to work extended hours, sacrificing health, family time, and social ties. The platform's gamified dispositif transforms pay uncertainty into consent, aligning worker subjectivity with algorithmic demands while externalizing risks onto drivers. Ethnographic observation of a May 2025 driver protest reveals the tensions underlying collective resistance. The protest was undermined when part-time drivers, less financially dependent on platforms, continued working.

Situating this analysis within Cluj's post-socialist urban transformation—characterized by gentrification, peri-urban displacement, and infrastructural inequalities—the paper shows how platforms extract value not only from drivers' labor but also from publicly funded infrastructure and social-reproductive mobility (school runs, shift commutes). Understanding these dynamics requires attending to both algorithmic governance and the uneven social positions through which workers experience and contest "good work."